Tencent's New Hy3 AI Model Is the Most Efficient Chinese LLM No One's Talking About

In brief

• Hy3 preview is a 295 billion parameter Mixture-of-Experts model with only 21 billion active parameters, making it cheaper to run than most rivals of similar capability.
• On SWE-bench Verified—a coding benchmark testing real GitHub bug fixes—it jumped from 53% (Hy2) to 74.4%, a 40% improvement over the previous generation.
• The model is already live across Tencent's app ecosystem including Yuanbao, QQ, and Tencent Docs, with API access on Tencent Cloud starting at roughly $0.18 per million input tokens. Tencent quietly dropped its most capable AI model yet on Thursday, and the benchmark numbers are hard to ignore. Hy3 preview, the company's first model after a full infrastructure rebuild, went open-source today across GitHub, Hugging Face, and ModelScope. It’s also available on Tencent Cloud’s official website, under a paid plan. My3 packs 295 billion total parameters (a measurement of a model’s potential breadth of knowledge) but only 21 billion active at any given time. That's the beauty of a Mixture-of-Experts architecture—the model routes each query to a specialized subset of its "expert" sub-networks instead of running everything at once. Less compute, lower cost, roughly similar output quality. It also supports up to 256,000 tokens of context, which is enough to swallow a full-length novel in a single prompt. The model was built to balance three things Tencent says it stopped sacrificing for each other: capability breadth, honest evaluation, and cost-efficiency. Their previous flagship, Hy2, had over 400 billion parameters. Tencent explicitly walked that back, arguing 295 billion is the optimal sweet spot where reasoning fully matures but the cost of adding more parameters stops paying off. This also doesn’t mean the model is worse. Models with better training and lower parameters outperform bigger generalist ones quite frequently. On coding, the improvement is dramatic. SWE-bench Verified is a benchmark that tests whether a model can actually fix real bugs from GitHub repositories—not toy problems, but production code. Hy2 scored 53.0%. Hy3 preview scores 74.4%. That's a 40% jump in one generation, landing it in range of Claude Opus 4.6 (80.8%) and above GLM-5 (77.8%) and Kimi-K2.5 (76.8%). Terminal-Bench 2.0, which measures autonomous task execution in a real command-line environment, went from 23.2% to 54.4%—also a massive leap. The model, however, can be a very interesting choice for people building with agents. Agents have a very complex set of instructions that involve memories, skills, and tool calls. They usually miss something, which can ruin a workflow or produce poor results. That’s why agentic capabilities are becoming more and more important for AI developers as this area becomes the most hyped thing in the industry. It’s also why the model was immediately on Openclaw.